scholarly journals A New Parallel Optimization Algorithm for Parameter Identification in Ordinary Differential Equations

1988 ◽  
Author(s):  
Jr Dennis ◽  
Williamson J. E. ◽  
Karen A.
Keyword(s):  
Differential Equations ◽  
Parameter Identification ◽  
Ordinary Differential Equations ◽  
Optimization Algorithm ◽  
Parallel Optimization

A Particle Swarm Optimization Algorithm and its Application in Hydrodynamic Equations

2012 ◽  
Vol 510 ◽  
pp. 472-477
Author(s):  
Jian Hui Zhou ◽  
Shu Zhong Zhao ◽  
Li Xi Yue ◽  
Yan Nan Lu ◽  
Xin Yi Si
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Fluid Mechanics ◽  
Ordinary Differential Equations ◽  
Optimization Algorithm ◽  
Multiple Solutions ◽  
Particle Swarm Optimization Algorithm ◽  
Swarm Optimization ◽  
Estimation Problems

In fluid mechanics, how to solve multiple solutions in ordinary differential equations is always a concerned and difficult problem. A particle swarm optimization algorithm combining with the direct search method (DSPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

scholarly journals COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Amir Mosavi
Keyword(s):  
Infectious Disease ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Gulf Cooperation Council ◽  
Disease Outbreak ◽  
Gcc Countries ◽  
Number Of Publications ◽  
Insight Into

AbstractExtension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm to fit 4-set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible. Trends of COVID-19 in Trends in Gulf Cooperation Council (GCC) countries are successfully predicted using available data from outbreak until 23rd June 2020. Promising results of SEIR-PAD model provide insight into better management of COVID-19 pandemic in GCC countries.

scholarly journals Trends of COVID-19 (Coronavirus Disease) in GCC Countries using SEIR-PAD Dynamic Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Seyed Amir Abbas Oloomi ◽  
Ashtian Malayer ◽  
Amir Mosavi
Keyword(s):  
Infectious Disease ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Dynamic Model ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Health Organizations ◽  
Policy Makers ◽  
Gcc Countries ◽  
Number Of Publications

Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

scholarly journals Nonlinear Parameter Identification for Ordinary Differential Equations

PAMM ◽  
2013 ◽  
Vol 13 (1) ◽  
pp. 457-458 ◽  
Author(s):  
Mitja Wöbbekind ◽  
Anna Kemper ◽  
Christof Büskens ◽  
Michael Schollmeyer
Keyword(s):  
Differential Equations ◽  
Parameter Identification ◽  
Ordinary Differential Equations ◽  
Nonlinear Parameter ◽  
Nonlinear Parameter Identification

scholarly journals Trends of COVID-19 (Coronavirus Disease) in GCC Countries using SEIR-PAD Dynamic Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Seyed Amir Abbas Oloomi ◽  
Mahdi Ashtian Malayer ◽  
Amir Mosavi
Keyword(s):  
Infectious Disease ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Dynamic Model ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Health Organizations ◽  
Policy Makers ◽  
Gcc Countries ◽  
Number Of Publications

AbstractExtension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

THE PARAMETER IDENTIFICATION PROBLEM FOR SYSTEM OF DIFFERENTIAL EQUATIONS

2021 ◽  
Vol 73 (1) ◽  
pp. 7-12
Author(s):  
A.T. Assanova ◽  
◽  
Ye. Shynarbek ◽  
Keyword(s):  
Differential Equations ◽  
Parameter Identification ◽  
Ordinary Differential Equations ◽  
Unique Solvability ◽  
Identification Problem ◽  
Identification Problems ◽  
Parameter Identification Problem ◽  
Parameter Identification Problems ◽  
And Control ◽  
Differential Part

In this paper, the parameter identification problem for system of ordinary differential equations is considered. The parameter identification problem for system of ordinary differential equations is investigated by the Dzhumabaev’s parametrization method. At first, conditions for a unique solvability of the parameter identification problem for system of ordinary differential equations are obtained in the term of fundamental matrix of system’s differential part. Further, we establish conditions for a unique solvability of the parameter identification problem for system of ordinary differential equations in the terms of initial data. Algorithm for finding of approximate solution to a unique solvability of the parameter identification problem for system of ordinary differential equations is proposed and the conditions for its convergence are setted. Results this paper can be use for investigating of various problems with parameter and control problems for system of ordinary differential equations. The approach in this paper can be apply to the parameter identification problems for partial differential equations.

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